Update README.md

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+datasets:
+- UniParser/MolDet-Bench
+base_model:
+- UniParser/MolDet
+- Ultralytics/YOLO11
+tags:
+- chemistry
+---
+
+
+# Molecule Detection YOLO in MolParser2.0
+
+Compared to [MolDet](https://huggingface.co/UniParser/MolDet), **MolDetv2** leverages more manually annotated training data, with further optimizations specifically for reducing molecular false detections and improving bounding box regression, achieving stronger performance with a smaller model.
+
+## [MolDet-General] General molecule structure detection models
+
+YOLO11-n weights trained on more than 100k human annotated image crops & synthesis molecule images.
+
+* 640x640 input resolution
+* support handwritten molecules detection
+* **multiscale input** (inputs can be single/multiple molecular cutouts, reaction or table cutouts, or single-page PDF images)
+* *update: MolDetv2 substantially reduces false positives on formulas, ball-and-stick diagrams, etc.*
+
+usage:
+```python
+from ultralytics import YOLO
+model = YOLO("/path/to/moldet_v2_yolo11n_640_general.pt") # for cpu only inference: using `moldet_v2_yolo11n_640_general.onnx` for faster speed
+model.predict("path/to/image.png", save=True, imgsz=640, conf=0.5)
+```
+For further usage instructions, please refer to the [official Ultralytics documentation](https://docs.ultralytics.com/modes/predict/).
+
+## [MolDet-Doc] PDF molecule structure detection models
+
+YOLO11-n weights trained on more than 60k human annotated PDF pages (patents, papers, and books) and 10k synthesis PDF pages with molecule images.
+
+* 960x960 input resolution
+* prefer **single page PDF image** input
+* better in small molecule detection
+* *update: MolDetv2 substantially reduces false positives on formulas, ball-and-stick diagrams, and graphical symbols, with tighter bounding box alignment to molecular edges.*
+
+usage:
+```python
+from ultralytics import YOLO
+import fitz # MuPDF
+pdf = fitz.open("doc.pdf")
+model = YOLO("/path/to/moldet_v2_yolo11n_960_doc.pt")  # for cpu only inference: using `moldet_v2_yolo11n_960_doc.onnx` for faster speed
+bboxes = []
+for i, p in enumerate(pdf):
+    img = f"page_{i}.png"; p.get_pixmap().save(img)
+    for r in model.predict(img, imgsz=960, conf=0.5):
+        for box in r.boxes:
+            bboxes.append({"page":img, "conf":float(box.conf), "bbox":box.xyxy[0].tolist()})
+```
+For further usage instructions, please refer to the [official Ultralytics documentation](https://docs.ultralytics.com/modes/predict/).
+
+## 📊 BenchMark Results
+
+Please refer to [MolDet-Bench](https://huggingface.co/datasets/UniParser/MolDet-Bench)
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/65f7f16fb6941db5c2e7c4bf/cVEMAL4xpy-y2vgmQ5mR-.png)
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/65f7f16fb6941db5c2e7c4bf/KHwlqpRZzTOlbOdCG7SyD.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/65f7f16fb6941db5c2e7c4bf/l-oNYAv3aGbqvGjImYstW.png)
+
+
+## 📖 Citation
+
+If you use this model in your work, please cite:
+
+```
+comming soon!
+```